{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer mini-batch R-Matrix (WIP)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/mini-batch_R-Matrix.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import torch\n",
    "from torch import nn\n",
    "import warnings\n",
    "\n",
    "notebook.tqdm.pandas()\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n",
      "Trainset saved.\n",
      "Retention calculated.\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.7          0.765        1.0     inf       7997\n",
      "            1,3           3.9          0.877        4.2    4.20       4174\n",
      "          1,3,3           8.5          0.882        9.1    2.17       2711\n",
      "        1,3,3,3          17.8          0.860       13.8    1.52       1619\n",
      "      1,3,3,3,3          36.4          0.833       22.3    1.62        845\n",
      "    1,3,3,3,3,3          74.7          0.861       36.4    1.63        402\n",
      "  1,3,3,3,3,3,3         118.2          0.895       38.5    1.06        182\n",
      "              2           1.0          0.901        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.3    7.55        201\n",
      "          2,3,3          11.1          0.890        7.1    0.86        160\n",
      "              3           1.5          0.962        5.4     inf       9134\n",
      "            3,3           3.9          0.966       15.2    2.81       6588\n",
      "          3,3,3           9.0          0.960       23.5    1.55       5166\n",
      "        3,3,3,3          19.1          0.941       43.5    1.85       3542\n",
      "      3,3,3,3,3          35.9          0.931       53.0    1.22       1976\n",
      "    3,3,3,3,3,3          65.0          0.928       97.6    1.84       1149\n",
      "  3,3,3,3,3,3,3         100.9          0.947      142.9    1.46        537\n",
      "3,3,3,3,3,3,3,3         133.6          0.990     1821.9   12.75        131\n",
      "              4           3.8          0.966       12.1     inf      11599\n",
      "            4,3           8.1          0.975       39.0    3.22       7515\n",
      "          4,3,3          17.8          0.963       56.5    1.45       5307\n",
      "        4,3,3,3          32.1          0.952       82.6    1.46       3032\n",
      "      4,3,3,3,3          46.3          0.956      125.4    1.52       1380\n",
      "    4,3,3,3,3,3          67.5          0.956      115.3    0.92        534\n",
      "  4,3,3,3,3,3,3          78.3          0.978      117.5    1.02        253\n",
      "4,3,3,3,3,3,3,3         114.1          0.984      177.8    1.51        166\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[(df['type'] != 3) | (df['factor'] != 0)].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "\n",
    "df['i'] = df.groupby('cid').cumcount() + 1\n",
    "df.loc[df['i'] == 1, 'delta_t'] = 0\n",
    "\n",
    "\n",
    "df = df.groupby('cid').filter(lambda group: group['type'].iloc[0] == 0)\n",
    "df['prev_type'] = df.groupby('cid')['type'].shift(1).fillna(0).astype(int)\n",
    "df['helper'] = (df['type'] == 0) & ((df['prev_type'] == 1) | (df['prev_type'] == 2)) & (df['i'] > 1)\n",
    "df['helper'] = df['helper'].astype(int)\n",
    "df['helper'] = df.groupby('cid')['helper'].cumsum()\n",
    "df = df[df['helper'] == 0]\n",
    "del df['prev_type']\n",
    "del df['helper']\n",
    "from itertools import accumulate\n",
    "\n",
    "def cum_concat(x):\n",
    "    return list(accumulate(x))\n",
    "\n",
    "t_history = df.groupby('cid', group_keys=False)['delta_t'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['t_history']=[','.join(map(str, item[:-1])) for sublist in t_history for item in sublist]\n",
    "r_history = df.groupby('cid', group_keys=False)['r'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['r_history']=[','.join(map(str, item[:-1])) for sublist in r_history for item in sublist]\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "df['y'] = df['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x])\n",
    "df['retention'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['y'].transform('mean')\n",
    "df['total_cnt'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['id'].transform('count')\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history', 'y'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 5, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2, 0.2, 0.2, 1, 0.5, 1]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w))\n",
    "\n",
    "    def stability_after_success(self, state, new_d, r):\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (11 - new_d) *\n",
    "                        torch.pow(state[:,0], -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, state, new_d, r):\n",
    "        new_s = self.w[9] * torch.pow(new_d, -self.w[10]) * torch.pow(\n",
    "            state[:,0], self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] - self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "        else:\n",
    "            r = torch.exp(np.log(0.9) * X[:,0] / state[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 10)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        new_s = new_s.clamp(0.1, 36500)\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "    def calc_r_matrix(self, train_set):\n",
    "        outputs, _ = self.forward(train_set.x_train.transpose(0, 1))\n",
    "        stabilities, difficulties = outputs[train_set.seq_len-1, torch.arange(len(train_set))].transpose(0, 1)\n",
    "        p = torch.exp(np.log(0.9) * train_set.t_train / stabilities)\n",
    "        y = train_set.y_train\n",
    "        r_matrix_raw = pd.DataFrame({'s': stabilities.detach().numpy(), 'd': difficulties.detach().numpy(), 'p': p.detach().numpy(), 'y': y.detach().numpy()})\n",
    "        r_matrix_raw['s_bin'] = r_matrix_raw['s'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "        r_matrix_raw['d_bin'] = r_matrix_raw['d'].map(lambda x: int(round(x)))\n",
    "        r_matrix_raw['r_bin'] = r_matrix_raw['p'].map(lambda x: round(x * 4, 1) / 4)\n",
    "        R_Matrix = r_matrix_raw.groupby(by=['s_bin', 'd_bin', 'r_bin']).agg({'y': ['mean', 'count']}).reset_index()\n",
    "        R_Matrix.columns = ['S_rounded', 'D_rounded', 'R_t_rounded', 'R_measured', 'count']\n",
    "        R_Matrix.set_index(['S_rounded', 'D_rounded', 'R_t_rounded'], inplace=True)\n",
    "        self.r_matrix = R_Matrix\n",
    "\n",
    "    def apply_r_matrix(self, stabilities, difficulties, p):\n",
    "        s_bins = list(map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2), stabilities.detach().numpy()))\n",
    "        d_bins = list(map(lambda x: int(round(x)), difficulties.detach().numpy()))\n",
    "        r_bins = list(map(lambda x: round(x * 4, 1) / 4, p.detach().numpy()))\n",
    "        index_tuples = list(zip(s_bins, d_bins, r_bins))\n",
    "        try:\n",
    "            r_m = torch.Tensor(self.r_matrix.loc[index_tuples, 'R_measured'].to_numpy())\n",
    "            n = self.r_matrix.loc[index_tuples, 'count'].map(lambda x: min(x, 300)).to_numpy()\n",
    "            weight = self.w[13] * torch.pow(torch.Tensor(n / 300), self.w[14])\n",
    "            r_w = r_m * weight + (1 - weight) * p\n",
    "        except KeyError:\n",
    "            r_w = p\n",
    "        return r_w\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 10)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0.05, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(0.15, 0.8)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            w[13] = w[13].clamp(0.01, 0.99)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from [self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist()]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=3, lr=4e-2, batch_size=512) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "        best_loss = np.inf\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        pbar = notebook.tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader) * self.n_epoch)\n",
    "        for k in range(self.n_epoch):\n",
    "            for i, batch in enumerate(self.pre_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(n=real_batch_size)\n",
    "        pbar.close()\n",
    "\n",
    "        for name, param in self.model.named_parameters():\n",
    "            print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "        for k in range(self.n_epoch):\n",
    "            weighted_loss, w = self.eval()\n",
    "            if weighted_loss < best_loss:\n",
    "                best_loss = weighted_loss\n",
    "                best_w = w\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    print(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "            \n",
    "\n",
    "            self.model.calc_r_matrix(self.train_set)\n",
    "            outputs, _ = self.model(self.train_set.x_train.transpose(0, 1))\n",
    "            stabilities, difficulties = outputs[self.train_set.seq_len-1, torch.arange(len(self.train_set))].transpose(0, 1)\n",
    "            p = torch.exp(np.log(0.9) * self.train_set.t_train / stabilities)\n",
    "            r_w = self.model.apply_r_matrix(stabilities, difficulties, p)\n",
    "            loss = self.loss_fn(r_w, self.train_set.y_train)\n",
    "            loss.backward()\n",
    "            self.optimizer.step()\n",
    "            self.model.apply(self.clipper)\n",
    "            \n",
    "        pbar.close()\n",
    "\n",
    "        weighted_loss, w = self.eval()\n",
    "        if weighted_loss < best_loss:\n",
    "            best_loss = weighted_loss\n",
    "            best_w = w\n",
    "\n",
    "        return best_w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sequences, delta_ts, labels, seq_lens = self.train_set.x_train, self.train_set.t_train, self.train_set.y_train, self.train_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            tran_loss = self.loss_fn(retentions, labels)/len(self.train_set)\n",
    "            self.avg_train_losses.append(tran_loss)\n",
    "            print(f\"Loss in trainset: {tran_loss:.4f}\")\n",
    "            \n",
    "            sequences, delta_ts, labels, seq_lens = self.test_set.x_train, self.test_set.t_train, self.test_set.y_train, self.test_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            test_loss = self.loss_fn(retentions, labels)/len(self.test_set)\n",
    "            self.avg_eval_losses.append(test_loss)\n",
    "            print(f\"Loss in testset: {test_loss:.4f}\")\n",
    "\n",
    "            w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "\n",
    "            weighted_loss = (tran_loss * len(self.train_set) + test_loss * len(self.test_set)) / (len(self.train_set) + len(self.test_set))\n",
    "\n",
    "            return weighted_loss, w\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c841a698da76451fa7f6ab9628ccb93c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/224893 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n",
      "TRAIN: 148471 TEST: 76422\n",
      "dataset built\n",
      "Loss in trainset: 0.3503\n",
      "Loss in testset: 0.3120\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a067c12d492459294d288231fd8535f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/52113 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0123, 2.1705, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0, 0.5, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d6975ba20404d9bb4606dfa87a2fb43",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/393300 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3483\n",
      "Loss in testset: 0.3063\n",
      "iteration: 39424\n",
      "w: [1.0373, 2.1598, 4.7984, 1.6584, 1.4238, 0.05, 1.4344, 0.1643, 0.9066, 1.686, 0.6075, 0.5083, 0.9803, 0.5, 1.0]\n",
      "iteration: 78848\n",
      "w: [1.0373, 2.1598, 4.4899, 1.5986, 1.4323, 0.05, 1.468, 0.155, 0.9607, 1.8656, 0.4414, 0.4856, 1.2683, 0.5, 1.0]\n",
      "iteration: 118272\n",
      "w: [1.0373, 2.1598, 4.3235, 1.634, 1.3268, 0.05, 1.3247, 0.1751, 0.8526, 1.8231, 0.4958, 0.5475, 1.2755, 0.5, 1.0]\n",
      "Loss in trainset: 0.3334\n",
      "Loss in testset: 0.2916\n",
      "iteration: 157212\n",
      "w: [1.1542, 2.5892, 4.2033, 1.5216, 1.366, 0.05, 1.391, 0.3542, 1.0252, 1.8377, 0.4713, 0.3037, 1.5986, 0.99, 0.4831]\n",
      "iteration: 196636\n",
      "w: [1.1547, 2.5908, 4.2005, 1.6736, 1.3884, 0.05, 1.3924, 0.3012, 1.0252, 1.8474, 0.4628, 0.41, 1.6184, 0.99, 0.481]\n",
      "iteration: 236060\n",
      "w: [1.1547, 2.5908, 4.1877, 1.6966, 1.3837, 0.05, 1.3866, 0.264, 1.017, 1.8313, 0.4795, 0.4394, 1.5876, 0.99, 0.481]\n",
      "Loss in trainset: 0.3314\n",
      "Loss in testset: 0.2892\n",
      "iteration: 275000\n",
      "w: [1.1657, 2.6091, 4.1819, 1.7986, 1.3388, 0.05, 1.3666, 0.2333, 0.9972, 1.853, 0.4597, 0.4865, 1.5959, 0.99, 0.2963]\n",
      "iteration: 314424\n",
      "w: [1.1663, 2.6101, 4.1998, 1.8421, 1.3518, 0.05, 1.3345, 0.2443, 0.9693, 1.8239, 0.4908, 0.4882, 1.5697, 0.99, 0.2857]\n",
      "iteration: 353848\n",
      "w: [1.1663, 2.6101, 4.192, 1.8314, 1.3424, 0.05, 1.3378, 0.2362, 0.9716, 1.8199, 0.4944, 0.4872, 1.5669, 0.99, 0.2857]\n",
      "iteration: 393272\n",
      "w: [1.1663, 2.6101, 4.1915, 1.8311, 1.3427, 0.05, 1.3376, 0.2352, 0.9714, 1.8188, 0.4955, 0.4869, 1.5654, 0.99, 0.2857]\n",
      "Loss in trainset: 0.3311\n",
      "Loss in testset: 0.2890\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 150549 TEST: 74344\n",
      "dataset built\n",
      "Loss in trainset: 0.3384\n",
      "Loss in testset: 0.3349\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "261b33db81444a238c0fa331ee0df799",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/59508 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0216, 2.7775, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0, 0.5, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e7792a3722c7479cbad55dc354e8335f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/392139 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3339\n",
      "Loss in testset: 0.3322\n",
      "iteration: 38912\n",
      "w: [1.0776, 2.855, 5.079, 1.8347, 1.5164, 0.05, 1.4132, 0.15, 0.9344, 1.7203, 0.5656, 0.5126, 1.2578, 0.5, 1.0]\n",
      "iteration: 77824\n",
      "w: [1.0776, 2.8551, 4.5518, 1.3686, 1.5528, 0.05, 1.3502, 0.2039, 0.9658, 1.7746, 0.5519, 0.4841, 1.3764, 0.5, 1.0]\n",
      "iteration: 116736\n",
      "w: [1.0776, 2.8551, 4.4132, 1.5026, 1.6977, 0.0523, 1.2535, 0.15, 0.8923, 1.7587, 0.5923, 0.6459, 1.2506, 0.5, 1.0]\n",
      "Loss in trainset: 0.3175\n",
      "Loss in testset: 0.3151\n",
      "iteration: 155289\n",
      "w: [1.1634, 3.2171, 4.2407, 1.7588, 1.5512, 0.05, 1.2524, 0.1544, 0.9097, 1.8566, 0.5467, 0.5014, 1.2634, 0.99, 0.4759]\n",
      "iteration: 194201\n",
      "w: [1.1638, 3.2191, 4.1627, 1.6598, 1.5152, 0.05, 1.2212, 0.1558, 0.8687, 1.8755, 0.5262, 0.5322, 1.2624, 0.99, 0.473]\n",
      "iteration: 233113\n",
      "w: [1.1638, 3.2191, 4.0791, 1.522, 1.4722, 0.05, 1.2078, 0.15, 0.8548, 1.913, 0.4872, 0.5822, 1.2657, 0.99, 0.473]\n",
      "Loss in trainset: 0.3169\n",
      "Loss in testset: 0.3146\n",
      "iteration: 271666\n",
      "w: [1.1752, 3.2482, 4.0476, 1.4737, 1.4785, 0.05, 1.195, 0.1504, 0.8361, 1.9297, 0.4681, 0.6055, 1.2682, 0.99, 0.291]\n",
      "iteration: 310578\n",
      "w: [1.1764, 3.2512, 4.052, 1.5033, 1.5167, 0.05, 1.1794, 0.15, 0.814, 1.8873, 0.5112, 0.5848, 1.2069, 0.99, 0.2719]\n",
      "iteration: 349490\n",
      "w: [1.1764, 3.2512, 4.0347, 1.489, 1.5127, 0.05, 1.1961, 0.1502, 0.8311, 1.8937, 0.504, 0.5898, 1.2111, 0.99, 0.2719]\n",
      "iteration: 388402\n",
      "w: [1.1764, 3.2512, 4.0363, 1.494, 1.5156, 0.05, 1.1932, 0.15, 0.8277, 1.8908, 0.5068, 0.5874, 1.2063, 0.99, 0.2719]\n",
      "Loss in trainset: 0.3167\n",
      "Loss in testset: 0.3144\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 150766 TEST: 74127\n",
      "dataset built\n",
      "Loss in trainset: 0.3233\n",
      "Loss in testset: 0.3657\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61a24a6b9a904080a77e639135f9a77d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/62199 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [2.2441, 2.455, 5.0, 0.5, 0.5, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 1.0, 0.5, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3377c98a7b9c44ff86d1fa5355b5be36",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/390099 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3191\n",
      "Loss in testset: 0.3700\n",
      "iteration: 38912\n",
      "w: [2.3014, 2.523, 4.931, 1.6008, 1.5345, 0.05, 1.4335, 0.15, 0.9383, 1.751, 0.4987, 0.5803, 1.2129, 0.5, 1.0]\n",
      "iteration: 77824\n",
      "w: [2.3014, 2.523, 4.7065, 1.5743, 1.4573, 0.05, 1.2842, 0.2197, 0.8026, 1.7944, 0.4584, 0.5567, 1.2579, 0.5, 1.0]\n",
      "iteration: 116736\n",
      "w: [2.3014, 2.523, 4.7822, 2.0749, 1.5531, 0.05, 1.3039, 0.15, 0.8458, 1.7225, 0.5811, 0.6202, 1.4835, 0.5, 1.0]\n",
      "Loss in trainset: 0.3020\n",
      "Loss in testset: 0.3542\n",
      "iteration: 155633\n",
      "w: [2.4382, 2.8331, 4.3342, 1.7388, 1.3656, 0.05, 1.3368, 0.2908, 0.9466, 1.8312, 0.5243, 0.5251, 1.6548, 0.99, 0.4732]\n",
      "iteration: 194545\n",
      "w: [2.4388, 2.8344, 4.3552, 1.847, 1.4281, 0.05, 1.2662, 0.2068, 0.8722, 1.7884, 0.5688, 0.5325, 1.6363, 0.99, 0.4709]\n",
      "iteration: 233457\n",
      "w: [2.4388, 2.8344, 4.3687, 1.9378, 1.4508, 0.0532, 1.265, 0.1605, 0.864, 1.8075, 0.5478, 0.5891, 1.6379, 0.99, 0.4709]\n",
      "Loss in trainset: 0.3017\n",
      "Loss in testset: 0.3544\n",
      "iteration: 272354\n",
      "w: [2.4852, 2.8657, 4.2854, 1.7925, 1.4026, 0.05, 1.2743, 0.157, 0.8667, 1.8287, 0.5256, 0.6298, 1.6429, 0.99, 0.2844]\n",
      "iteration: 311266\n",
      "w: [2.4882, 2.8677, 4.2934, 1.7779, 1.4206, 0.05, 1.2421, 0.1637, 0.8301, 1.8118, 0.5426, 0.6103, 1.6211, 0.99, 0.2723]\n",
      "iteration: 350178\n",
      "w: [2.4882, 2.8677, 4.3016, 1.7961, 1.4383, 0.05, 1.2371, 0.1525, 0.8234, 1.8025, 0.5518, 0.6054, 1.6112, 0.99, 0.2723]\n",
      "iteration: 389090\n",
      "w: [2.4882, 2.8677, 4.297, 1.7962, 1.4355, 0.05, 1.2426, 0.15, 0.8291, 1.8019, 0.5524, 0.6063, 1.6103, 0.99, 0.2723]\n",
      "Loss in trainset: 0.3016\n",
      "Loss in testset: 0.3543\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=3)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=3, lr=4e-2, batch_size=512)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.6103, 2.9097, 4.1749, 1.7071, 1.4313, 0.05, 1.2578, 0.1784, 0.8761, 1.8372, 0.5182, 0.5602, 1.4607, 0.99, 0.2766]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,3d,5d,10d,23d,1.1m,1.3m,2.9m,6.8m,1.1y,1.3y\n",
      "difficulty history: 0,7.6,7.4,7.3,7.1,7.0,6.8,6.7,6.6,6.4,6.3\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,8d,21d,29d,4.0m,2.4m,9.2m,1.1y,1.8y,2.8y,4.4y\n",
      "difficulty history: 0,5.9,5.8,5.7,5.6,5.6,5.5,5.4,5.4,5.3,5.3\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,15d,1.2m,4.7m,1.9y,2.1y,3.5y,5.7y,8.9y,13.6y,20.1y\n",
      "difficulty history: 0,4.2,4.2,4.2,4.2,4.2,4.2,4.2,4.2,4.2,4.2\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,25d,2.5m,11.7m,1.4y,2.7y,4.8y,8.1y,13.1y,20.4y,31.0y\n",
      "difficulty history: 0,2.5,2.6,2.6,2.7,2.8,2.9,2.9,3.0,3.0,3.1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w, dataset):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "        self.model.calc_r_matrix(RevlogDataset(dataset))\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        fast_dataset = RevlogDataset(dataset)\n",
    "        outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "        stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "        return stabilities.tolist(), difficulties.tolist()\n",
    "    \n",
    "    def apply_r_matrix(self, stabilities, difficulties, p):\n",
    "        if type(stabilities) == list:\n",
    "            r_w = self.model.apply_r_matrix(torch.Tensor(stabilities), torch.Tensor(difficulties), torch.Tensor(p))\n",
    "            s_w = torch.Tensor(stabilities) * torch.log(torch.Tensor(p)) / torch.log(r_w)\n",
    "        else:\n",
    "            r_w = self.model.apply_r_matrix(stabilities.unsqueeze(0), difficulties.unsqueeze(0), torch.Tensor([p]))\n",
    "            s_w = stabilities * math.log(p) / torch.log(r_w)\n",
    "        return r_w, s_w\n",
    "\n",
    "my_collection = Collection(avg_w, dataset)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        stability, difficulty = my_collection.predict(t_history, r_history)\n",
    "        r_w, s_w = my_collection.apply_r_matrix(stability, difficulty, requestRetention)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(round(float(np.log(requestRetention)/np.log(0.9) * s_w)), 1)\n",
    "        difficulty = round(float(difficulty), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(\"interval history: \" + \",\".join([f\"{ivl}d\" if ivl < 30 else f\"{ivl / 30:.1f}m\" if ivl < 365 else f\"{ivl / 365:.1f}y\" for ivl in map(int, t_history.split(','))]))\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(7.4297) tensor(4.1749)\n",
      "tensor(30.2277) tensor(4.1749)\n",
      "tensor(72.0464) tensor(4.1749)\n",
      "tensor(214.0579) tensor(4.1749)\n",
      "tensor(773.9893) tensor(4.1749)\n",
      "tensor(32.4606) tensor(6.8944)\n",
      "tensor(4.6282) tensor(9.4779)\n",
      "tensor(6.8164) tensor(9.2127)\n",
      "tensor(10.5409) tensor(8.9608)\n",
      "tensor(14.8857) tensor(8.7215)\n",
      "tensor(19.3886) tensor(8.4942)\n",
      "tensor(28.1804) tensor(8.2782)\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,15,35,140,682,774,31,5,8,9,9,17,19\n",
      "difficulty history: 0,4.2,4.2,4.2,4.2,4.2,6.9,9.5,9.2,9.0,8.7,8.5,8.3\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    stability, difficulty = my_collection.predict(t_history, r_history)\n",
    "    r_w, s_w = my_collection.apply_r_matrix(stability, difficulty, requestRetention)\n",
    "    print(stability, difficulty)\n",
    "    next_t = max(1,round(float(np.log(requestRetention)/np.log(0.9) * s_w)))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(difficulty), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.043025\n",
      "2     0.057996\n",
      "3     0.103969\n",
      "4     0.161228\n",
      "5     0.040041\n",
      "6     0.042509\n",
      "7     0.097033\n",
      "8     0.103832\n",
      "9     0.148946\n",
      "10    0.201420\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(10)\n",
    "for i in range(10):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  1017.83\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e40bf9e67f1465eaf2ac4d1ed8aa8b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "find optimal retention:   0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.86-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "minimum_stability = 0.1\n",
    "index_offset = -(np.log(minimum_stability) / np.log(base)).round().astype(int)\n",
    "d_range = 10\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 1e10\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return (np.log(stability) / np.log(base)).round().astype(int) + index_offset\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + 2) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + np.exp(avg_w[6]) * (11 - d) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * np.power(s, avg_w[11]) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "terminal_stability = minimum_stability\n",
    "for _ in range(128):\n",
    "    terminal_stability = cal_next_recall_stability(terminal_stability, 0.96, 10, 1)\n",
    "index_len = stability2index(terminal_stability)\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2), desc=\"find optimal retention\"):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        iteration = 0\n",
    "        while diff > 1 and iteration < 2e5:\n",
    "            iteration += 1\n",
    "            total_time = time_list[d - 1].sum()\n",
    "            s_indices = np.arange(index_len - 2, -1, -1)\n",
    "            stabilities = stability_list[s_indices]\n",
    "            intervals = np.maximum(1, np.round(stabilities * np.log(recall) / np.log(0.9)))\n",
    "            p_recalls = np.power(0.9, intervals / stabilities)\n",
    "            recall_s = cal_next_recall_stability(stabilities, p_recalls, d, 1)\n",
    "            forget_d = np.minimum(d + d_offset, 10)\n",
    "            forget_s = cal_next_recall_stability(stabilities, p_recalls, forget_d, 0)\n",
    "            recall_s_indices = np.minimum(stability2index(recall_s), index_len - 1)\n",
    "            forget_s_indices = np.clip(stability2index(forget_s), 0, index_len - 1)\n",
    "            recall_times = time_list[d - 1][recall_s_indices] + r_time\n",
    "            forget_times = time_list[forget_d - 1][forget_s_indices] + f_time\n",
    "            exp_times = p_recalls * recall_times + (1.0 - p_recalls) * forget_times\n",
    "            mask = exp_times <= time_list[d - 1][s_indices]\n",
    "            time_list[d - 1][s_indices[mask]] = exp_times[mask]\n",
    "            diff = total_time - time_list[d - 1].sum()\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            if iteration % 1e5 == 0:\n",
    "                print(f\"iteration: {iteration}, diff: {diff}, s0_time: {s0_time}\")\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(10)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3373\n",
      "Loss after training: 0.3053\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w, dataset)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w, dataset)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "p = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "r_w, s_w = my_collection.apply_r_matrix(stabilities, difficulties, p.to_numpy())\n",
    "dataset['p'] = r_w.tolist()\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9893\n",
      "RMSE: 0.0077\n",
      "[-0.00931226  1.01060465]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts, squared=False)\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    fit_wls = sm.WLS(bin_correct_means, sm.add_constant(bin_prediction_means), weights=bin_counts).fit()\n",
    "    print(fit_wls.params)\n",
    "    y_regression = [fit_wls.params[0] + fit_wls.params[1]*x for x in bin_prediction_means]\n",
    "    plt.plot(bin_prediction_means, y_regression, label='Weighted Least Squares Regression', color=\"green\")\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration', color=\"#1f77b4\")\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration', color=\"#ff7f0e\")\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "difficulty_calibration = pd.DataFrame(columns=['difficulty', 'predicted_retention', 'actual_retention'])\n",
    "difficulty_calibration = dataset[['difficulty', 'p', 'y']].copy()\n",
    "difficulty_calibration['bin'] = difficulty_calibration['difficulty'].map(round)\n",
    "difficulty_group = difficulty_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=difficulty_group.index, height=difficulty_group['y'],\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Difficulty\")\n",
    "lns.append(lns1)\n",
    "\n",
    "difficulty_group = difficulty_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(difficulty_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(difficulty_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  color: #f1f1f1;\n",
       "}\n",
       "#T_91a13_row0_col9, #T_91a13_row4_col7, #T_91a13_row11_col0, #T_91a13_row14_col0, #T_91a13_row16_col3, #T_91a13_row17_col2 {\n",
       "  background-color: #f5f5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row1_col9, #T_91a13_row2_col9, #T_91a13_row3_col7, #T_91a13_row5_col4, #T_91a13_row5_col6, #T_91a13_row5_col8, #T_91a13_row5_col9, #T_91a13_row6_col8, #T_91a13_row7_col3, #T_91a13_row7_col6, #T_91a13_row7_col7, #T_91a13_row7_col8, #T_91a13_row8_col3, #T_91a13_row8_col4, #T_91a13_row8_col7, #T_91a13_row9_col2, #T_91a13_row9_col3, #T_91a13_row10_col0, #T_91a13_row10_col2, #T_91a13_row10_col4, #T_91a13_row10_col6, #T_91a13_row11_col3, #T_91a13_row12_col0, #T_91a13_row12_col2, #T_91a13_row13_col0, #T_91a13_row13_col2, #T_91a13_row14_col2, #T_91a13_row14_col3 {\n",
       "  background-color: #fdfdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row3_col9, #T_91a13_row4_col6, #T_91a13_row4_col9, #T_91a13_row6_col4, #T_91a13_row6_col7, #T_91a13_row6_col9, #T_91a13_row7_col4, #T_91a13_row8_col1, #T_91a13_row8_col5, #T_91a13_row9_col4, #T_91a13_row11_col2, #T_91a13_row15_col0, #T_91a13_row15_col2 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row4_col8, #T_91a13_row7_col9, #T_91a13_row8_col8, #T_91a13_row9_col5, #T_91a13_row9_col8, #T_91a13_row9_col9, #T_91a13_row10_col3, #T_91a13_row11_col8, #T_91a13_row12_col3, #T_91a13_row13_col3, #T_91a13_row13_col5 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row5_col7, #T_91a13_row7_col5 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row6_col6, #T_91a13_row16_col0, #T_91a13_row16_col2 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row8_col6, #T_91a13_row8_col9, #T_91a13_row13_col8 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row9_col6, #T_91a13_row9_col7, #T_91a13_row10_col7, #T_91a13_row10_col8, #T_91a13_row10_col9, #T_91a13_row11_col4, #T_91a13_row11_col5, #T_91a13_row11_col7, #T_91a13_row12_col4, #T_91a13_row12_col6, #T_91a13_row12_col8, #T_91a13_row13_col6, #T_91a13_row14_col4, #T_91a13_row14_col5, #T_91a13_row15_col6 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row10_col5, #T_91a13_row12_col7, #T_91a13_row12_col9, #T_91a13_row14_col7, #T_91a13_row15_col4 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row11_col6, #T_91a13_row11_col9, #T_91a13_row13_col4, #T_91a13_row14_col6 {\n",
       "  background-color: #fff1f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row12_col5 {\n",
       "  background-color: #ffe5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row13_col7 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row15_col3, #T_91a13_row18_col2 {\n",
       "  background-color: #ededff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_91a13_row17_col3 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_91a13\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th id=\"T_91a13_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_91a13_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_91a13_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_91a13_level0_col3\" class=\"col_heading level0 col3\" >4</th>\n",
       "      <th id=\"T_91a13_level0_col4\" class=\"col_heading level0 col4\" >5</th>\n",
       "      <th id=\"T_91a13_level0_col5\" class=\"col_heading level0 col5\" >6</th>\n",
       "      <th id=\"T_91a13_level0_col6\" class=\"col_heading level0 col6\" >7</th>\n",
       "      <th id=\"T_91a13_level0_col7\" class=\"col_heading level0 col7\" >8</th>\n",
       "      <th id=\"T_91a13_level0_col8\" class=\"col_heading level0 col8\" >9</th>\n",
       "      <th id=\"T_91a13_level0_col9\" class=\"col_heading level0 col9\" >10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row0\" class=\"row_heading level0 row0\" >0.510000</th>\n",
       "      <td id=\"T_91a13_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_91a13_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_91a13_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_91a13_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_91a13_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_91a13_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_91a13_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_91a13_row0_col7\" class=\"data row0 col7\" ></td>\n",
       "      <td id=\"T_91a13_row0_col8\" class=\"data row0 col8\" ></td>\n",
       "      <td id=\"T_91a13_row0_col9\" class=\"data row0 col9\" >-0.35%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row1\" class=\"row_heading level0 row1\" >0.710000</th>\n",
       "      <td id=\"T_91a13_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_91a13_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_91a13_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_91a13_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_91a13_row1_col4\" class=\"data row1 col4\" ></td>\n",
       "      <td id=\"T_91a13_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_91a13_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_91a13_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_91a13_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_91a13_row1_col9\" class=\"data row1 col9\" >-0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row2\" class=\"row_heading level0 row2\" >1.000000</th>\n",
       "      <td id=\"T_91a13_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_91a13_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_91a13_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_91a13_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_91a13_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_91a13_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_91a13_row2_col6\" class=\"data row2 col6\" ></td>\n",
       "      <td id=\"T_91a13_row2_col7\" class=\"data row2 col7\" ></td>\n",
       "      <td id=\"T_91a13_row2_col8\" class=\"data row2 col8\" ></td>\n",
       "      <td id=\"T_91a13_row2_col9\" class=\"data row2 col9\" >-0.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row3\" class=\"row_heading level0 row3\" >1.400000</th>\n",
       "      <td id=\"T_91a13_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_91a13_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_91a13_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_91a13_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_91a13_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_91a13_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_91a13_row3_col6\" class=\"data row3 col6\" ></td>\n",
       "      <td id=\"T_91a13_row3_col7\" class=\"data row3 col7\" >-0.00%</td>\n",
       "      <td id=\"T_91a13_row3_col8\" class=\"data row3 col8\" ></td>\n",
       "      <td id=\"T_91a13_row3_col9\" class=\"data row3 col9\" >-0.23%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row4\" class=\"row_heading level0 row4\" >1.960000</th>\n",
       "      <td id=\"T_91a13_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_91a13_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_91a13_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_91a13_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_91a13_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_91a13_row4_col5\" class=\"data row4 col5\" ></td>\n",
       "      <td id=\"T_91a13_row4_col6\" class=\"data row4 col6\" >-0.27%</td>\n",
       "      <td id=\"T_91a13_row4_col7\" class=\"data row4 col7\" >-0.43%</td>\n",
       "      <td id=\"T_91a13_row4_col8\" class=\"data row4 col8\" >0.01%</td>\n",
       "      <td id=\"T_91a13_row4_col9\" class=\"data row4 col9\" >-0.24%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row5\" class=\"row_heading level0 row5\" >2.740000</th>\n",
       "      <td id=\"T_91a13_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_91a13_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_91a13_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_91a13_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_91a13_row5_col4\" class=\"data row5 col4\" >-0.00%</td>\n",
       "      <td id=\"T_91a13_row5_col5\" class=\"data row5 col5\" ></td>\n",
       "      <td id=\"T_91a13_row5_col6\" class=\"data row5 col6\" >-0.13%</td>\n",
       "      <td id=\"T_91a13_row5_col7\" class=\"data row5 col7\" >-0.88%</td>\n",
       "      <td id=\"T_91a13_row5_col8\" class=\"data row5 col8\" >-0.01%</td>\n",
       "      <td id=\"T_91a13_row5_col9\" class=\"data row5 col9\" >-0.14%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row6\" class=\"row_heading level0 row6\" >3.840000</th>\n",
       "      <td id=\"T_91a13_row6_col0\" class=\"data row6 col0\" ></td>\n",
       "      <td id=\"T_91a13_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_91a13_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_91a13_row6_col3\" class=\"data row6 col3\" ></td>\n",
       "      <td id=\"T_91a13_row6_col4\" class=\"data row6 col4\" >-0.17%</td>\n",
       "      <td id=\"T_91a13_row6_col5\" class=\"data row6 col5\" ></td>\n",
       "      <td id=\"T_91a13_row6_col6\" class=\"data row6 col6\" >-0.48%</td>\n",
       "      <td id=\"T_91a13_row6_col7\" class=\"data row6 col7\" >-0.16%</td>\n",
       "      <td id=\"T_91a13_row6_col8\" class=\"data row6 col8\" >-0.13%</td>\n",
       "      <td id=\"T_91a13_row6_col9\" class=\"data row6 col9\" >-0.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row7\" class=\"row_heading level0 row7\" >5.380000</th>\n",
       "      <td id=\"T_91a13_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_91a13_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_91a13_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_91a13_row7_col3\" class=\"data row7 col3\" >-0.06%</td>\n",
       "      <td id=\"T_91a13_row7_col4\" class=\"data row7 col4\" >-0.21%</td>\n",
       "      <td id=\"T_91a13_row7_col5\" class=\"data row7 col5\" >-0.85%</td>\n",
       "      <td id=\"T_91a13_row7_col6\" class=\"data row7 col6\" >-0.05%</td>\n",
       "      <td id=\"T_91a13_row7_col7\" class=\"data row7 col7\" >-0.03%</td>\n",
       "      <td id=\"T_91a13_row7_col8\" class=\"data row7 col8\" >-0.11%</td>\n",
       "      <td id=\"T_91a13_row7_col9\" class=\"data row7 col9\" >0.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row8\" class=\"row_heading level0 row8\" >7.530000</th>\n",
       "      <td id=\"T_91a13_row8_col0\" class=\"data row8 col0\" ></td>\n",
       "      <td id=\"T_91a13_row8_col1\" class=\"data row8 col1\" >-0.16%</td>\n",
       "      <td id=\"T_91a13_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_91a13_row8_col3\" class=\"data row8 col3\" >-0.03%</td>\n",
       "      <td id=\"T_91a13_row8_col4\" class=\"data row8 col4\" >-0.03%</td>\n",
       "      <td id=\"T_91a13_row8_col5\" class=\"data row8 col5\" >-0.21%</td>\n",
       "      <td id=\"T_91a13_row8_col6\" class=\"data row8 col6\" >0.38%</td>\n",
       "      <td id=\"T_91a13_row8_col7\" class=\"data row8 col7\" >-0.11%</td>\n",
       "      <td id=\"T_91a13_row8_col8\" class=\"data row8 col8\" >0.04%</td>\n",
       "      <td id=\"T_91a13_row8_col9\" class=\"data row8 col9\" >0.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row9\" class=\"row_heading level0 row9\" >10.540000</th>\n",
       "      <td id=\"T_91a13_row9_col0\" class=\"data row9 col0\" ></td>\n",
       "      <td id=\"T_91a13_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_91a13_row9_col2\" class=\"data row9 col2\" >-0.14%</td>\n",
       "      <td id=\"T_91a13_row9_col3\" class=\"data row9 col3\" >-0.08%</td>\n",
       "      <td id=\"T_91a13_row9_col4\" class=\"data row9 col4\" >-0.25%</td>\n",
       "      <td id=\"T_91a13_row9_col5\" class=\"data row9 col5\" >0.10%</td>\n",
       "      <td id=\"T_91a13_row9_col6\" class=\"data row9 col6\" >0.20%</td>\n",
       "      <td id=\"T_91a13_row9_col7\" class=\"data row9 col7\" >0.23%</td>\n",
       "      <td id=\"T_91a13_row9_col8\" class=\"data row9 col8\" >0.14%</td>\n",
       "      <td id=\"T_91a13_row9_col9\" class=\"data row9 col9\" >0.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row10\" class=\"row_heading level0 row10\" >14.760000</th>\n",
       "      <td id=\"T_91a13_row10_col0\" class=\"data row10 col0\" >-0.05%</td>\n",
       "      <td id=\"T_91a13_row10_col1\" class=\"data row10 col1\" ></td>\n",
       "      <td id=\"T_91a13_row10_col2\" class=\"data row10 col2\" >-0.05%</td>\n",
       "      <td id=\"T_91a13_row10_col3\" class=\"data row10 col3\" >0.06%</td>\n",
       "      <td id=\"T_91a13_row10_col4\" class=\"data row10 col4\" >-0.14%</td>\n",
       "      <td id=\"T_91a13_row10_col5\" class=\"data row10 col5\" >0.75%</td>\n",
       "      <td id=\"T_91a13_row10_col6\" class=\"data row10 col6\" >-0.02%</td>\n",
       "      <td id=\"T_91a13_row10_col7\" class=\"data row10 col7\" >0.17%</td>\n",
       "      <td id=\"T_91a13_row10_col8\" class=\"data row10 col8\" >0.16%</td>\n",
       "      <td id=\"T_91a13_row10_col9\" class=\"data row10 col9\" >0.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row11\" class=\"row_heading level0 row11\" >20.660000</th>\n",
       "      <td id=\"T_91a13_row11_col0\" class=\"data row11 col0\" >-0.43%</td>\n",
       "      <td id=\"T_91a13_row11_col1\" class=\"data row11 col1\" ></td>\n",
       "      <td id=\"T_91a13_row11_col2\" class=\"data row11 col2\" >-0.24%</td>\n",
       "      <td id=\"T_91a13_row11_col3\" class=\"data row11 col3\" >-0.06%</td>\n",
       "      <td id=\"T_91a13_row11_col4\" class=\"data row11 col4\" >0.24%</td>\n",
       "      <td id=\"T_91a13_row11_col5\" class=\"data row11 col5\" >0.27%</td>\n",
       "      <td id=\"T_91a13_row11_col6\" class=\"data row11 col6\" >0.53%</td>\n",
       "      <td id=\"T_91a13_row11_col7\" class=\"data row11 col7\" >0.22%</td>\n",
       "      <td id=\"T_91a13_row11_col8\" class=\"data row11 col8\" >0.02%</td>\n",
       "      <td id=\"T_91a13_row11_col9\" class=\"data row11 col9\" >0.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row12\" class=\"row_heading level0 row12\" >28.930000</th>\n",
       "      <td id=\"T_91a13_row12_col0\" class=\"data row12 col0\" >-0.14%</td>\n",
       "      <td id=\"T_91a13_row12_col1\" class=\"data row12 col1\" ></td>\n",
       "      <td id=\"T_91a13_row12_col2\" class=\"data row12 col2\" >-0.08%</td>\n",
       "      <td id=\"T_91a13_row12_col3\" class=\"data row12 col3\" >0.07%</td>\n",
       "      <td id=\"T_91a13_row12_col4\" class=\"data row12 col4\" >0.31%</td>\n",
       "      <td id=\"T_91a13_row12_col5\" class=\"data row12 col5\" >1.02%</td>\n",
       "      <td id=\"T_91a13_row12_col6\" class=\"data row12 col6\" >0.27%</td>\n",
       "      <td id=\"T_91a13_row12_col7\" class=\"data row12 col7\" >0.66%</td>\n",
       "      <td id=\"T_91a13_row12_col8\" class=\"data row12 col8\" >0.19%</td>\n",
       "      <td id=\"T_91a13_row12_col9\" class=\"data row12 col9\" >0.74%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row13\" class=\"row_heading level0 row13\" >40.500000</th>\n",
       "      <td id=\"T_91a13_row13_col0\" class=\"data row13 col0\" >-0.14%</td>\n",
       "      <td id=\"T_91a13_row13_col1\" class=\"data row13 col1\" ></td>\n",
       "      <td id=\"T_91a13_row13_col2\" class=\"data row13 col2\" >-0.06%</td>\n",
       "      <td id=\"T_91a13_row13_col3\" class=\"data row13 col3\" >0.09%</td>\n",
       "      <td id=\"T_91a13_row13_col4\" class=\"data row13 col4\" >0.61%</td>\n",
       "      <td id=\"T_91a13_row13_col5\" class=\"data row13 col5\" >0.07%</td>\n",
       "      <td id=\"T_91a13_row13_col6\" class=\"data row13 col6\" >0.30%</td>\n",
       "      <td id=\"T_91a13_row13_col7\" class=\"data row13 col7\" >0.85%</td>\n",
       "      <td id=\"T_91a13_row13_col8\" class=\"data row13 col8\" >0.45%</td>\n",
       "      <td id=\"T_91a13_row13_col9\" class=\"data row13 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row14\" class=\"row_heading level0 row14\" >56.690000</th>\n",
       "      <td id=\"T_91a13_row14_col0\" class=\"data row14 col0\" >-0.34%</td>\n",
       "      <td id=\"T_91a13_row14_col1\" class=\"data row14 col1\" ></td>\n",
       "      <td id=\"T_91a13_row14_col2\" class=\"data row14 col2\" >-0.00%</td>\n",
       "      <td id=\"T_91a13_row14_col3\" class=\"data row14 col3\" >-0.11%</td>\n",
       "      <td id=\"T_91a13_row14_col4\" class=\"data row14 col4\" >0.25%</td>\n",
       "      <td id=\"T_91a13_row14_col5\" class=\"data row14 col5\" >0.29%</td>\n",
       "      <td id=\"T_91a13_row14_col6\" class=\"data row14 col6\" >0.54%</td>\n",
       "      <td id=\"T_91a13_row14_col7\" class=\"data row14 col7\" >0.70%</td>\n",
       "      <td id=\"T_91a13_row14_col8\" class=\"data row14 col8\" ></td>\n",
       "      <td id=\"T_91a13_row14_col9\" class=\"data row14 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row15\" class=\"row_heading level0 row15\" >79.370000</th>\n",
       "      <td id=\"T_91a13_row15_col0\" class=\"data row15 col0\" >-0.22%</td>\n",
       "      <td id=\"T_91a13_row15_col1\" class=\"data row15 col1\" ></td>\n",
       "      <td id=\"T_91a13_row15_col2\" class=\"data row15 col2\" >-0.20%</td>\n",
       "      <td id=\"T_91a13_row15_col3\" class=\"data row15 col3\" >-0.69%</td>\n",
       "      <td id=\"T_91a13_row15_col4\" class=\"data row15 col4\" >0.71%</td>\n",
       "      <td id=\"T_91a13_row15_col5\" class=\"data row15 col5\" ></td>\n",
       "      <td id=\"T_91a13_row15_col6\" class=\"data row15 col6\" >0.27%</td>\n",
       "      <td id=\"T_91a13_row15_col7\" class=\"data row15 col7\" ></td>\n",
       "      <td id=\"T_91a13_row15_col8\" class=\"data row15 col8\" ></td>\n",
       "      <td id=\"T_91a13_row15_col9\" class=\"data row15 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row16\" class=\"row_heading level0 row16\" >111.120000</th>\n",
       "      <td id=\"T_91a13_row16_col0\" class=\"data row16 col0\" >-0.57%</td>\n",
       "      <td id=\"T_91a13_row16_col1\" class=\"data row16 col1\" ></td>\n",
       "      <td id=\"T_91a13_row16_col2\" class=\"data row16 col2\" >-0.54%</td>\n",
       "      <td id=\"T_91a13_row16_col3\" class=\"data row16 col3\" >-0.34%</td>\n",
       "      <td id=\"T_91a13_row16_col4\" class=\"data row16 col4\" ></td>\n",
       "      <td id=\"T_91a13_row16_col5\" class=\"data row16 col5\" ></td>\n",
       "      <td id=\"T_91a13_row16_col6\" class=\"data row16 col6\" ></td>\n",
       "      <td id=\"T_91a13_row16_col7\" class=\"data row16 col7\" ></td>\n",
       "      <td id=\"T_91a13_row16_col8\" class=\"data row16 col8\" ></td>\n",
       "      <td id=\"T_91a13_row16_col9\" class=\"data row16 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row17\" class=\"row_heading level0 row17\" >155.570000</th>\n",
       "      <td id=\"T_91a13_row17_col0\" class=\"data row17 col0\" ></td>\n",
       "      <td id=\"T_91a13_row17_col1\" class=\"data row17 col1\" ></td>\n",
       "      <td id=\"T_91a13_row17_col2\" class=\"data row17 col2\" >-0.35%</td>\n",
       "      <td id=\"T_91a13_row17_col3\" class=\"data row17 col3\" >-1.02%</td>\n",
       "      <td id=\"T_91a13_row17_col4\" class=\"data row17 col4\" ></td>\n",
       "      <td id=\"T_91a13_row17_col5\" class=\"data row17 col5\" ></td>\n",
       "      <td id=\"T_91a13_row17_col6\" class=\"data row17 col6\" ></td>\n",
       "      <td id=\"T_91a13_row17_col7\" class=\"data row17 col7\" ></td>\n",
       "      <td id=\"T_91a13_row17_col8\" class=\"data row17 col8\" ></td>\n",
       "      <td id=\"T_91a13_row17_col9\" class=\"data row17 col9\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_91a13_level0_row18\" class=\"row_heading level0 row18\" >217.800000</th>\n",
       "      <td id=\"T_91a13_row18_col0\" class=\"data row18 col0\" ></td>\n",
       "      <td id=\"T_91a13_row18_col1\" class=\"data row18 col1\" ></td>\n",
       "      <td id=\"T_91a13_row18_col2\" class=\"data row18 col2\" >-0.65%</td>\n",
       "      <td id=\"T_91a13_row18_col3\" class=\"data row18 col3\" ></td>\n",
       "      <td id=\"T_91a13_row18_col4\" class=\"data row18 col4\" ></td>\n",
       "      <td id=\"T_91a13_row18_col5\" class=\"data row18 col5\" ></td>\n",
       "      <td id=\"T_91a13_row18_col6\" class=\"data row18 col6\" ></td>\n",
       "      <td id=\"T_91a13_row18_col7\" class=\"data row18 col7\" ></td>\n",
       "      <td id=\"T_91a13_row18_col8\" class=\"data row18 col8\" ></td>\n",
       "      <td id=\"T_91a13_row18_col9\" class=\"data row18 col9\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b78b2190>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DiffMax: 0.012704918032786838\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S_rounded</th>\n",
       "      <th>D_rounded</th>\n",
       "      <th>R_t_rounded</th>\n",
       "      <th>R_measured</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.51</td>\n",
       "      <td>10</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.768212</td>\n",
       "      <td>755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.71</td>\n",
       "      <td>10</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.802210</td>\n",
       "      <td>1810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.71</td>\n",
       "      <td>10</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.881473</td>\n",
       "      <td>869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1.00</td>\n",
       "      <td>10</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.663014</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1.00</td>\n",
       "      <td>10</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.760797</td>\n",
       "      <td>602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>79.37</td>\n",
       "      <td>4</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.946399</td>\n",
       "      <td>597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>111.12</td>\n",
       "      <td>1</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.969770</td>\n",
       "      <td>827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>111.12</td>\n",
       "      <td>4</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.946918</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362</th>\n",
       "      <td>155.57</td>\n",
       "      <td>3</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.955504</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1422</th>\n",
       "      <td>217.80</td>\n",
       "      <td>3</td>\n",
       "      <td>0.975</td>\n",
       "      <td>0.982097</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>176 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      S_rounded  D_rounded  R_t_rounded  R_measured  count\n",
       "12         0.51         10        0.775    0.768212    755\n",
       "28         0.71         10        0.800    0.802210   1810\n",
       "29         0.71         10        0.875    0.881473    869\n",
       "47         1.00         10        0.675    0.663014    365\n",
       "50         1.00         10        0.750    0.760797    602\n",
       "...         ...        ...          ...         ...    ...\n",
       "1193      79.37          4        0.950    0.946399    597\n",
       "1270     111.12          1        0.975    0.969770    827\n",
       "1295     111.12          4        0.950    0.946918    584\n",
       "1362     155.57          3        0.950    0.955504    427\n",
       "1422     217.80          3        0.975    0.982097    391\n",
       "\n",
       "[176 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw['r_bin'] = B_W_Metric_raw['p'].map(lambda x: round(x * 4, 1) / 4)\n",
    "R_Matrix = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin', 'r_bin']).agg({'y': ['mean', 'count']}).reset_index()\n",
    "R_Matrix.columns = ['S_rounded', 'D_rounded', 'R_t_rounded', 'R_measured', 'count']\n",
    "R_Matrix = R_Matrix[R_Matrix['count'] > 300]\n",
    "print(\"DiffMax:\", max(abs(R_Matrix['R_t_rounded'] - R_Matrix['R_measured'])))\n",
    "R_Matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Comparision with Anki bulit-in algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anki(history):\n",
    "    ivl = 0\n",
    "    start_ease = 2.5\n",
    "    hard_interval = 1.2\n",
    "    ease_bonus = 1.3\n",
    "    graduating_interval = 1\n",
    "    easy_interval = 4\n",
    "    new_interval = 0\n",
    "    minimum_interval = 1\n",
    "    interval_modifier = 1\n",
    "    for i, (delta_t, rating) in enumerate(history):\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item()\n",
    "        if i == 0:\n",
    "            ease = start_ease\n",
    "            if rating == 4:\n",
    "                ivl = easy_interval\n",
    "            else:\n",
    "                ivl = graduating_interval\n",
    "        else:\n",
    "            delay = delta_t - ivl\n",
    "            if delay >= 0:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = ivl * hard_interval\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = (ivl + delay // 2) * ease\n",
    "                else:\n",
    "                    ivl = (ivl + delay) * ease * ease_bonus\n",
    "                    ease += 0.15\n",
    "            # early_review\n",
    "            else:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = max(delta_t * hard_interval / 2, ivl * hard_interval)\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = max(delta_t * ease, ivl)\n",
    "                else:\n",
    "                    ivl = max(delta_t * ease, ivl) * (0.5 * ease_bonus + 0.5)\n",
    "                    ease += 0.15\n",
    "            ivl = ivl * interval_modifier\n",
    "        ease = max(1.3, ease)\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "def sm2(history):\n",
    "    ivl = 0\n",
    "    ef = 2.5\n",
    "    reps = 0\n",
    "    for delta_t, rating in history:\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item() + 1\n",
    "        if rating > 2:\n",
    "            if reps == 0:\n",
    "                ivl = 1\n",
    "                reps = 1\n",
    "            elif reps == 1:\n",
    "                ivl = 6\n",
    "                reps = 2\n",
    "            else:\n",
    "                ivl = ivl * ef\n",
    "                reps += 1\n",
    "        else:\n",
    "            ivl = 1\n",
    "            reps = 0\n",
    "        ef = max(1.3, ef + (0.1 - (5 - rating) * (0.08 + (5 - rating) * 0.02)))\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "dataset['sm2_interval'] = dataset['tensor'].map(sm2)\n",
    "dataset['sm2_p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['sm2_interval'])\n",
    "cross_comparison = dataset[['sm2_p', 'p', 'y']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_Metric:  0.03898382127185934\n",
      "R-squared: -21.5980\n",
      "RMSE: 0.1408\n",
      "[0.76478601 0.15818509]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R_Metric = mean_squared_error(cross_comparison['y'], cross_comparison['sm2_p'], squared=False) - mean_squared_error(cross_comparison['y'], cross_comparison['p'], squared=False)\n",
    "print(\"R_Metric: \", R_Metric)\n",
    "plot_brier(dataset['sm2_p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Metric of FSRS: 0.0067\n",
      "Universal Metric of SM2: 0.0728\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "cross_comparison['SM2_B-W'] = cross_comparison['sm2_p'] - cross_comparison['y']\n",
    "cross_comparison['SM2_bin'] = cross_comparison['sm2_p'].map(lambda x: round(x, 1))\n",
    "cross_comparison['FSRS_B-W'] = cross_comparison['p'] - cross_comparison['y']\n",
    "cross_comparison['FSRS_bin'] = cross_comparison['p'].map(lambda x: round(x, 1))\n",
    "\n",
    "plt.axhline(y = 0.0, color = 'black', linestyle = '-')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='SM2_bin').agg({'y': ['mean'], 'FSRS_B-W': ['mean'], 'p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of FSRS: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['p', 'mean'], sample_weight=cross_comparison_group['p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['p', 'percent'] = cross_comparison_group['p', 'count'] / cross_comparison_group['p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['FSRS_B-W', 'mean'], s=cross_comparison_group['p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['FSRS_B-W', 'mean'], label='FSRS by SM2')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='FSRS_bin').agg({'y': ['mean'], 'SM2_B-W': ['mean'], 'sm2_p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of SM2: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['sm2_p', 'mean'], sample_weight=cross_comparison_group['sm2_p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['sm2_p', 'percent'] = cross_comparison_group['sm2_p', 'count'] / cross_comparison_group['sm2_p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['SM2_B-W', 'mean'], s=cross_comparison_group['sm2_p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['SM2_B-W', 'mean'], label='SM2 by FSRS')\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.grid(linestyle='--')\n",
    "plt.title(\"SM2 vs. FSRS\")\n",
    "plt.xlabel('Predicted R')\n",
    "plt.ylabel('B-W Metric')\n",
    "plt.xlim(0, 1)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.show()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "fsrs4anki",
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    "name": "ipython",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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